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  1. 1 point
    The above method for combining a Forecast Signal and a Measured Signal should work in any release of Seeq. New in R21 of Seeq however, we have the "forecastSplice()" function which greatly simplifies the workflow. As before, we need a Measured Data signal and some Forecast Signal that will be combined with the Measured Data. The Seeq R21 and later workflow: 1. Create the Master Signal In formula, use the new forecastSplice() operator to join the Measured Data and the Forecast Signal $measuredData.forecastsplice($forecastSignal) The Master Signal now appears as solid line where points are known, and dashed line where the points are still uncertain and expected to change. This is slightly different from the view shown above where the entire Master Signal was dashed due to uncertainty of future data. This helps the Seeq user to visually see where the Measured Data ends and where our Forecast Signal begins. Just as the above formula, the forecastSplice() will update as new data comes in Another operator, forecastConstant() was also introduced with R21. This would do something similar to what is shown above, however, instead of combining the Forecast Signal with the Measured Data, forecastConstant() would project the last value for the Measured Data into the future for some specified amount of time i.e. $measuredData.forecastConstant(1d) would create a Master Signal where the forecast is projected 1 day into the future: 2. Make sure the Master Signal is Auto-Updating Same as in the prior post
  2. 1 point
    Seeq is often used to contextualize data with respect to production runs. These product runs may be a text or string signal that is the product code, or a very large numerical signal. Users commonly use Value Search to find a specific product run to further analyze. If they want to work with a couple of similar product runs, for example ones that start with or end with the same few letters or numbers, a few Value Searches followed by Composite Condition may be acceptable. This approach may not be realistic if there are hundreds of different product codes to analyze. Recently a user asked for a trim function because they wanted to categorize all product codes by the first few letters the product code. For example, ABC-123-XYZ and ABC-456-DEF would both fall under the "ABC" product category. In Excel, users might use something like the functions LEFT and RIGHT to return the first few characters (LEFT(3) in this ABC example). One way to do this text or string manipulation in Seeq is to use the replace() function with a regular expression. Regular expressions can be intimidating to those who have not used them before, but they can also be very powerful. A little exploration on sites like https://regex101.com can help evaluate what kind of regular expression is appropriate for a specific use case. Given the above example product codes, the below Seeq Formula incorporates a regular expression within the replace() function to parse the string signal by the "-" and then return only the first part of that parsed string based on the "$1". $productcode.replace('/(.*)-(.*)-(.*)/', '$1') I could similarly categorize by the last three characters with a function like $productcode.replace('/(.*)-(.*)-(.*)/', '$3') Once this simplified text signal is available, any other tools can be used in the analysis. If the product code was a very large number instead of a string, apply toString() to benefit from the replace() function. There are often many ways to solve a problem. An alternate approach to categorize product codes like this might be to pair toCapsules() and filter() off the Value property in Formula. Perhaps the best solution is incorporating regular expressions into Value Search like in the example below to create conditions any time the product code starts with ABC (/^ABC.*/) or any time it ends with XYZ (/.*XYZ$/). The slashes here indicate regular expressions should be used, similar to searching with regex in the Data Pane. But this approach is likely not obvious or easy without a little experience with regular expressions. So while regular expressions may feel foreign at first, do not be intimidated! They really can pay off in the long run.
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